{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) For every experiment, all the hyperparameters and data are kept constant, except the optimizers and the associated hypo-parameters. \n",
    "\n",
    "2) The optimizers mentioned include sgd, adagrad, adam and rmsprop.\n",
    "\n",
    "3) The code is adopted and modified from https://github.com/caffe2/caffe2/blob/master/caffe2/python/tutorials/MNIST.ipynb\n",
    "\n",
    "4) Data initialization and network building are to be ignored in this tutorial. Only things to be considered are the optimizer being used, its parameters and how the loss_vs_accuracy curve developes during the training of mnist classifier with "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.\n",
      "WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Necessities imported!\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "import os\n",
    "import shutil\n",
    "import caffe2.python.predictor.predictor_exporter as pe\n",
    "from caffe2.python import (\n",
    "    brew,\n",
    "    core,\n",
    "    model_helper,\n",
    "    net_drawer,\n",
    "    optimizer,\n",
    "    visualize,\n",
    "    workspace,\n",
    ")\n",
    "from IPython.display import Markdown\n",
    "from ipy_table import *\n",
    "# If you would like to see some really detailed initializations,\n",
    "# you can change --caffe2_log_level=0 to --caffe2_log_level=-1\n",
    "core.GlobalInit(['caffe2', '--caffe2_log_level=0'])\n",
    "\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "print(\"Necessities imported!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**lmdb train db found!**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**lmdb test db found!**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Looks like you ran this before, so we need to cleanup those old files..."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**data folder:**/Users/litebot/caffe2_notebooks/tutorial_data/mnist"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**workspace root folder:**/Users/litebot/caffe2_notebooks/tutorial_files/tutorial_mnist"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download Data\n",
    "\n",
    "# Adopted from: https://caffe2.ai/docs/tutorial-MNIST.html\n",
    "\n",
    "def DownloadResource(url, path):\n",
    "    '''Downloads resources from s3 by url and unzips them to the provided path'''\n",
    "    import requests, zipfile, StringIO\n",
    "    print(\"Downloading... {} to {}\".format(url, path))\n",
    "    r = requests.get(url, stream=True)\n",
    "    z = zipfile.ZipFile(StringIO.StringIO(r.content))\n",
    "    z.extractall(path)\n",
    "    print(\"Completed download and extraction.\")\n",
    "    \n",
    "current_folder = os.path.join(os.path.expanduser('~'), 'caffe2_notebooks')\n",
    "data_folder = os.path.join(current_folder, 'tutorial_data', 'mnist')\n",
    "root_folder = os.path.join(current_folder, 'tutorial_files', 'tutorial_mnist')\n",
    "db_missing = False\n",
    "\n",
    "if not os.path.exists(data_folder):\n",
    "    os.makedirs(data_folder)   \n",
    "    print(\"Your data folder was not found!! This was generated: {}\".format(data_folder))\n",
    "\n",
    "# Look for existing database: lmdb\n",
    "if os.path.exists(os.path.join(data_folder,\"mnist-train-nchw-lmdb\")):\n",
    "    printmd(\"**lmdb train db found!**\")\n",
    "else:\n",
    "    db_missing = True\n",
    "\n",
    "if os.path.exists(os.path.join(data_folder,\"mnist-test-nchw-lmdb\")):\n",
    "    printmd(\"**lmdb test db found!**\")\n",
    "else:\n",
    "    db_missing = True\n",
    "\n",
    "# attempt the download of the db if either was missing\n",
    "if db_missing:\n",
    "    print(\"one or both of the MNIST lmbd dbs not found!!\")\n",
    "    db_url = \"http://download.caffe2.ai/databases/mnist-lmdb.zip\"\n",
    "    try:\n",
    "        DownloadResource(db_url, data_folder)\n",
    "    except Exception as ex:\n",
    "        print(\"Failed to download dataset. Please download it manually from {}\".format(db_url))\n",
    "        print(\"Unzip it and place the two database folders here: {}\".format(data_folder))\n",
    "        raise ex\n",
    "\n",
    "if os.path.exists(root_folder):\n",
    "    printmd(\"Looks like you ran this before, so we need to cleanup those old files...\")\n",
    "    shutil.rmtree(root_folder)\n",
    "\n",
    "os.makedirs(root_folder)\n",
    "workspace.ResetWorkspace(root_folder)\n",
    "\n",
    "printmd(\"**data folder:**\" + data_folder)\n",
    "printmd(\"**workspace root folder:**\" + root_folder)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lmdb train db found!\n",
      "lmdb test db found!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This section downloads and preps your train and test set in a lmdb database\n",
    "\n",
    "def DownloadResource(url, path):\n",
    "    '''Downloads resources from s3 by url and unzips them to the provided path'''\n",
    "    import requests, zipfile, StringIO\n",
    "    print(\"Downloading... {} to {}\".format(url, path))\n",
    "    r = requests.get(url, stream=True)\n",
    "    z = zipfile.ZipFile(StringIO.StringIO(r.content))\n",
    "    z.extractall(path)\n",
    "    print(\"Completed download and extraction.\")\n",
    "\n",
    "\n",
    "current_folder = os.path.join(os.path.expanduser('~'), 'caffe2_notebooks')\n",
    "data_folder = os.path.join(current_folder, 'tutorial_data', 'mnist')\n",
    "root_folder = os.path.join(current_folder, 'tutorial_files', 'tutorial_mnist')\n",
    "db_missing = False\n",
    "\n",
    "if not os.path.exists(data_folder):\n",
    "    os.makedirs(data_folder)   \n",
    "    print(\"Your data folder was not found!! This was generated: {}\".format(data_folder))\n",
    "\n",
    "# Look for existing database: lmdb\n",
    "if os.path.exists(os.path.join(data_folder,\"mnist-train-nchw-lmdb\")):\n",
    "    print(\"lmdb train db found!\")\n",
    "else:\n",
    "    db_missing = True\n",
    "\n",
    "if os.path.exists(os.path.join(data_folder,\"mnist-test-nchw-lmdb\")):\n",
    "    print(\"lmdb test db found!\")\n",
    "else:\n",
    "    db_missing = True\n",
    "\n",
    "# attempt the download of the db if either was missing\n",
    "if db_missing:\n",
    "    print(\"one or both of the MNIST lmbd dbs not found!!\")\n",
    "    db_url = \"http://download.caffe2.ai/databases/mnist-lmdb.zip\"\n",
    "    try:\n",
    "        DownloadResource(db_url, data_folder)\n",
    "    except Exception as ex:\n",
    "        print(\"Failed to download dataset. Please download it manually from {}\".format(db_url))\n",
    "        print(\"Unzip it and place the two database folders here: {}\".format(data_folder))\n",
    "        raise ex\n",
    "\n",
    "if os.path.exists(root_folder):\n",
    "    #print(\"Looks like you ran this before, so we need to cleanup those old files...\")\n",
    "    shutil.rmtree(root_folder)\n",
    "\n",
    "os.makedirs(root_folder)\n",
    "workspace.ResetWorkspace(root_folder)\n",
    "\n",
    "#print(\"training data folder:\" + data_folder)\n",
    "#print(\"workspace root folder:\" + root_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Adding input to blob\n",
    "# 2. Creating network\n",
    "# 3. Adding accuracy parameter\n",
    "\n",
    "\n",
    "def AddInput(model, batch_size, db, db_type):\n",
    "    # load the data\n",
    "    data_uint8, label = brew.db_input(\n",
    "        model,\n",
    "        blobs_out=[\"data_uint8\", \"label\"],\n",
    "        batch_size=batch_size,\n",
    "        db=db,\n",
    "        db_type=db_type,\n",
    "    )\n",
    "    # cast the data to float\n",
    "    data = model.Cast(data_uint8, \"data\", to=core.DataType.FLOAT)\n",
    "    # scale data from [0,255] down to [0,1]\n",
    "    data = model.Scale(data, data, scale=float(1./256))\n",
    "    # don't need the gradient for the backward pass\n",
    "    data = model.StopGradient(data, data)\n",
    "    return data, label\n",
    "\n",
    "def AddLeNetModel(model, data):\n",
    "    '''\n",
    "    This part is the standard LeNet model: from data to the softmax prediction.\n",
    "    \n",
    "    For each convolutional layer we specify dim_in - number of input channels\n",
    "    and dim_out - number or output channels. Also each Conv and MaxPool layer changes the\n",
    "    image size. For example, kernel of size 5 reduces each side of an image by 4.\n",
    "\n",
    "    While when we have kernel and stride sizes equal 2 in a MaxPool layer, it divides\n",
    "    each side in half.\n",
    "    '''\n",
    "    # Image size: 28 x 28 -> 24 x 24\n",
    "    conv1 = brew.conv(model, data, 'conv1', dim_in=1, dim_out=20, kernel=5)\n",
    "    # Image size: 24 x 24 -> 12 x 12\n",
    "    pool1 = model.net.MaxPool(conv1, 'pool1', kernel=2, stride=2)\n",
    "    # Image size: 12 x 12 -> 8 x 8\n",
    "    conv2 = brew.conv(model, pool1, 'conv2', dim_in=20, dim_out=50, kernel=5)\n",
    "    # Image size: 8 x 8 -> 4 x 4\n",
    "    pool2 = model.net.MaxPool(conv2, 'pool2', kernel=2, stride=2)\n",
    "    # 50 * 4 * 4 stands for dim_out from previous layer multiplied by the image size\n",
    "    fc3 = brew.fc(model, pool2, 'fc3', dim_in=50 * 4 * 4, dim_out=500)\n",
    "    fc3 = model.net.Relu(fc3, 'relu3')\n",
    "    pred = brew.fc(model, fc3, 'pred', 500, 10)\n",
    "    softmax = model.net.Softmax(pred, 'softmax')\n",
    "    return softmax\n",
    "\n",
    "\n",
    "def AddAccuracy(model, softmax, label):\n",
    "    \"\"\"Adds an accuracy op to the model\"\"\"\n",
    "    accuracy = model.Accuracy([softmax, label], \"accuracy\")\n",
    "    return accuracy\n",
    "\n",
    "def AddModel(model, data):\n",
    "    return AddLeNetModel(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Solver: Stochastic gradient descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Helper function db_input not registered.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-9691aed1d3e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mtrain_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mdb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mnist-train-nchw-lmdb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     db_type='lmdb')\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0msoftmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAddModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mAddTrainingOperators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msoftmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-77be2361a622>\u001b[0m in \u001b[0;36mAddInput\u001b[0;34m(model, batch_size, db, db_type)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mAddInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdb_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# load the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     data_uint8, label = brew.db_input(\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mblobs_out\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"data_uint8\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"label\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/caffe2/python/brew.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, helper_name)\u001b[0m\n\u001b[1;32m     90\u001b[0m             raise AttributeError(\n\u001b[1;32m     91\u001b[0m                 \u001b[0;34m\"Helper function {} not \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m                 \u001b[0;34m\"registered.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhelper_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m             )\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: Helper function db_input not registered."
     ]
    }
   ],
   "source": [
    "base_lr = 0.001\n",
    "m_high = 0.9\n",
    "def AddTrainingOperators(model, softmax, label):\n",
    "    xent = model.LabelCrossEntropy([softmax, label], 'xent')\n",
    "    loss = model.AveragedLoss(xent, \"loss\")\n",
    "    AddAccuracy(model, softmax, label)\n",
    "    model.AddGradientOperators([loss])\n",
    "    optimizer.build_sgd(\n",
    "        model,\n",
    "        nesterov=1,\n",
    "        momentum=m_high,\n",
    "        base_learning_rate=base_lr,\n",
    "        policy=\"step\",\n",
    "        stepsize=1,\n",
    "        gamma=0.999,\n",
    "    )\n",
    "arg_scope = {\"order\": \"NCHW\"}\n",
    "train_model = model_helper.ModelHelper(name=\"mnist_train\", arg_scope=arg_scope)\n",
    "data, label = AddInput(\n",
    "    train_model, batch_size=64,\n",
    "    db=os.path.join(data_folder, 'mnist-train-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(train_model, data)\n",
    "AddTrainingOperators(train_model, softmax, label)\n",
    "workspace.ResetWorkspace()\n",
    "workspace.RunNetOnce(train_model.param_init_net)\n",
    "workspace.CreateNet(train_model.net, overwrite=True)\n",
    "total_iters_sgd = 2000\n",
    "accuracy_sgd = np.zeros(total_iters_sgd)\n",
    "loss_sgd = np.zeros(total_iters_sgd)\n",
    "printmd('**Training status: Running**  Wait till the process is completed')\n",
    "for i in range(total_iters_sgd):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed iterations:\", i, \", Total iterations to be completed:\", total_iters_sgd\n",
    "    workspace.RunNet(train_model.net)\n",
    "    accuracy_sgd[i] = workspace.blobs['accuracy']\n",
    "    loss_sgd[i] = workspace.blobs['loss']\n",
    "#print \"Training completed\"\n",
    "printmd('**Training status: Completed**')\n",
    "\n",
    "#print \"Testing status: Running\"\n",
    "printmd('**Testing status: Running**  Wait till the process is completed')\n",
    "test_model = model_helper.ModelHelper(\n",
    "    name=\"mnist_test\", arg_scope=arg_scope, init_params=False)\n",
    "data, label = AddInput(\n",
    "    test_model, batch_size=100,\n",
    "    db=os.path.join(data_folder, 'mnist-test-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(test_model, data)\n",
    "AddAccuracy(test_model, softmax, label)\n",
    "workspace.RunNetOnce(test_model.param_init_net)\n",
    "workspace.CreateNet(test_model.net, overwrite=True)\n",
    "test_iters_sgd = 1000\n",
    "test_accuracy_sgd = np.zeros(test_iters_sgd)\n",
    "for i in range(test_iters_sgd):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed test set:\", i, \", Total sets to be tested:\", test_iters_sgd\n",
    "    workspace.RunNet(test_model.net.Proto().name)\n",
    "    test_accuracy_sgd[i] = workspace.FetchBlob('accuracy')\n",
    "#print \"Testing completed\"\n",
    "printmd('**Testing status: Completed**')\n",
    "# After the execution is done, let's plot the values.\n",
    "pyplot.plot(loss_sgd, 'b')\n",
    "pyplot.plot(accuracy_sgd, 'r')\n",
    "pyplot.legend(('Loss', 'Accuracy'), loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Effects of optimizers**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\"  style=\"border:black; border-collapse:collapse;\"><tr><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Solver&nbsp;Type</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Num&nbsp;iterations</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;LR</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Testing-Acc(%)</b></td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">SGD</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3395</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1706</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">10.9375</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.1900</td></tr></table>"
      ],
      "text/plain": [
       "<ipy_table.ipy_table.IpyTable at 0x7fd6180e3190>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('**Effects of optimizers**')\n",
    "\n",
    "solvers = [\n",
    "    ['Solver Type', 'Num iterations', 'Init LR', 'Init Loss', 'Final Loss', 'Init Training-Acc(%)', \\\n",
    "     'Final Training-Acc(%)', 'Final Testing-Acc(%)'],\n",
    "    ['SGD', total_iters_sgd, base_lr, loss_sgd[0], loss_sgd[total_iters_sgd-1], accuracy_sgd[0]*100, \\\n",
    "     accuracy_sgd[total_iters_sgd-1]*100, np.mean(test_accuracy_sgd)*100],\n",
    "\n",
    "];\n",
    "make_table(solvers)\n",
    "apply_theme('basic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver: Multi-precision SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Training status: Running**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed iterations: 0 , Total iterations to be completed 2000\n",
      "    Completed iterations: 100 , Total iterations to be completed 2000\n",
      "    Completed iterations: 200 , Total iterations to be completed 2000\n",
      "    Completed iterations: 300 , Total iterations to be completed 2000\n",
      "    Completed iterations: 400 , Total iterations to be completed 2000\n",
      "    Completed iterations: 500 , Total iterations to be completed 2000\n",
      "    Completed iterations: 600 , Total iterations to be completed 2000\n",
      "    Completed iterations: 700 , Total iterations to be completed 2000\n",
      "    Completed iterations: 800 , Total iterations to be completed 2000\n",
      "    Completed iterations: 900 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1000 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1100 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1200 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1300 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1400 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1500 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1600 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1700 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1800 , Total iterations to be completed 2000\n",
      "    Completed iterations: 1900 , Total iterations to be completed 2000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Training status: Completed**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Running**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed test set: 0 , Total sets to be tested 1000\n",
      "    Completed test set: 100 , Total sets to be tested 1000\n",
      "    Completed test set: 200 , Total sets to be tested 1000\n",
      "    Completed test set: 300 , Total sets to be tested 1000\n",
      "    Completed test set: 400 , Total sets to be tested 1000\n",
      "    Completed test set: 500 , Total sets to be tested 1000\n",
      "    Completed test set: 600 , Total sets to be tested 1000\n",
      "    Completed test set: 700 , Total sets to be tested 1000\n",
      "    Completed test set: 800 , Total sets to be tested 1000\n",
      "    Completed test set: 900 , Total sets to be tested 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Completed**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ed5e6627ad0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5e7345e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.001\n",
    "m_high = 0.9\n",
    "def AddTrainingOperators(model, softmax, label):\n",
    "    xent = model.LabelCrossEntropy([softmax, label], 'xent')\n",
    "    loss = model.AveragedLoss(xent, \"loss\")\n",
    "    AddAccuracy(model, softmax, label)\n",
    "    model.AddGradientOperators([loss])\n",
    "    optimizer.build_multi_precision_sgd(\n",
    "        model,\n",
    "        nesterov=1,\n",
    "        momentum=m_high,\n",
    "        base_learning_rate=base_lr,\n",
    "        policy=\"step\",\n",
    "        stepsize=1,\n",
    "        gamma=0.999,\n",
    "    )\n",
    "arg_scope = {\"order\": \"NCHW\"}\n",
    "train_model = model_helper.ModelHelper(name=\"mnist_train\", arg_scope=arg_scope)\n",
    "data, label = AddInput(\n",
    "    train_model, batch_size=64,\n",
    "    db=os.path.join(data_folder, 'mnist-train-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(train_model, data)\n",
    "AddTrainingOperators(train_model, softmax, label)\n",
    "workspace.RunNetOnce(train_model.param_init_net)\n",
    "workspace.CreateNet(train_model.net, overwrite=True)\n",
    "total_iters_sgd_pr = 2000\n",
    "accuracy_sgd_pr = np.zeros(total_iters_sgd_pr)\n",
    "loss_sgd_pr = np.zeros(total_iters_sgd_pr)\n",
    "printmd('**Training status: Running**  Wait till the process is completed')\n",
    "for i in range(total_iters_sgd_pr):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed iterations:\", i, \", Total iterations to be completed:\", total_iters_sgd_pr\n",
    "    workspace.RunNet(train_model.net)\n",
    "    accuracy_sgd_pr[i] = workspace.blobs['accuracy']\n",
    "    loss_sgd_pr[i] = workspace.blobs['loss']\n",
    "#print \"Training completed\"\n",
    "printmd('**Training status: Completed**')\n",
    "\n",
    "#print \"Testing status: Running\"\n",
    "printmd('**Testing status: Running**  Wait till the process is completed')\n",
    "test_model = model_helper.ModelHelper(\n",
    "    name=\"mnist_test\", arg_scope=arg_scope, init_params=False)\n",
    "data, label = AddInput(\n",
    "    test_model, batch_size=100,\n",
    "    db=os.path.join(data_folder, 'mnist-test-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(test_model, data)\n",
    "AddAccuracy(test_model, softmax, label)\n",
    "workspace.RunNetOnce(test_model.param_init_net)\n",
    "workspace.CreateNet(test_model.net, overwrite=True)\n",
    "test_iters_sgd_pr = 1000\n",
    "test_accuracy_sgd_pr = np.zeros(test_iters_sgd_pr)\n",
    "for i in range(test_iters_sgd_pr):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed test set:\", i, \", Total sets to be tested:\", test_iters_sgd_pr\n",
    "    workspace.RunNet(test_model.net.Proto().name)\n",
    "    test_accuracy_sgd_pr[i] = workspace.FetchBlob('accuracy')\n",
    "#print \"Testing completed\"\n",
    "printmd('**Testing status: Completed**')\n",
    "# After the execution is done, let's plot the values.\n",
    "pyplot.plot(loss_sgd_pr, 'b')\n",
    "pyplot.plot(accuracy_sgd_pr, 'r')\n",
    "pyplot.legend(('Loss', 'Accuracy'), loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Effects of optimizers**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\"  style=\"border:black; border-collapse:collapse;\"><tr><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Solver&nbsp;Type</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Num&nbsp;iterations</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;LR</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Testing-Acc(%)</b></td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">SGD</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3395</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1706</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">10.9375</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.1900</td></tr><tr><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">Multi-Precision-SGD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3698</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1656</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">9.3750</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.5000</td></tr></table>"
      ],
      "text/plain": [
       "<ipy_table.ipy_table.IpyTable at 0x7ed5e6476790>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e4071e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('**Effects of optimizers**')\n",
    "\n",
    "pyplot.figure(figsize=(10, 5))\n",
    "pyplot.subplot(2,1,1)\n",
    "pyplot.plot(loss_sgd, 'b')\n",
    "pyplot.plot(accuracy_sgd, 'r')\n",
    "pyplot.legend(('Loss_sgd', 'Accuracy_sgd'), loc='upper right')\n",
    "\n",
    "\n",
    "pyplot.subplot(2,1,2)\n",
    "pyplot.plot(loss_sgd_pr, 'b')\n",
    "pyplot.plot(accuracy_sgd_pr, 'r')\n",
    "pyplot.legend(('Loss_sgd_pr', 'Accuracy_sgd_pr'), loc='upper right')\n",
    "\n",
    "\n",
    "solvers = [\n",
    "    ['Solver Type', 'Num iterations', 'Init LR', 'Init Loss', 'Final Loss', 'Init Training-Acc(%)', \\\n",
    "     'Final Training-Acc(%)', 'Final Testing-Acc(%)'],\n",
    "    ['SGD', total_iters_sgd, base_lr, loss_sgd[0], loss_sgd[total_iters_sgd-1], accuracy_sgd[0]*100, \\\n",
    "     accuracy_sgd[total_iters_sgd-1]*100, np.mean(test_accuracy_sgd)*100],\n",
    "    ['Multi-Precision-SGD', total_iters_sgd, base_lr, loss_sgd_pr[0], loss_sgd_pr[total_iters_sgd_pr-1], \\\n",
    "     accuracy_sgd_pr[0]*100, accuracy_sgd_pr[total_iters_sgd_pr-1]*100, np.mean(test_accuracy_sgd_pr)*100]\n",
    "\n",
    "];\n",
    "make_table(solvers)\n",
    "apply_theme('basic')\n",
    "set_cell_style(2, 7, color='red')\n",
    "set_cell_style(2, 0, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver: Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Training status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed iterations: 0 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 100 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 200 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 300 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 400 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 500 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 600 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 700 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 800 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 900 , Total iterations to be completed: 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Training status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed test set: 0 , Total sets to be tested: 1000\n",
      "    Completed test set: 100 , Total sets to be tested: 1000\n",
      "    Completed test set: 200 , Total sets to be tested: 1000\n",
      "    Completed test set: 300 , Total sets to be tested: 1000\n",
      "    Completed test set: 400 , Total sets to be tested: 1000\n",
      "    Completed test set: 500 , Total sets to be tested: 1000\n",
      "    Completed test set: 600 , Total sets to be tested: 1000\n",
      "    Completed test set: 700 , Total sets to be tested: 1000\n",
      "    Completed test set: 800 , Total sets to be tested: 1000\n",
      "    Completed test set: 900 , Total sets to be tested: 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ed5e4190790>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e71946d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.001\n",
    "def AddTrainingOperators(model, softmax, label):\n",
    "    xent = model.LabelCrossEntropy([softmax, label], 'xent')\n",
    "    loss = model.AveragedLoss(xent, \"loss\")\n",
    "    AddAccuracy(model, softmax, label)\n",
    "    model.AddGradientOperators([loss])\n",
    "    optimizer.build_adam(\n",
    "        model,\n",
    "        base_learning_rate=base_lr, \n",
    "        beta1=0.9, \n",
    "        beta2=0.999, \n",
    "        epsilon=1e-8,\n",
    "        policy=\"step\",\n",
    "        stepsize=1,\n",
    "        gamma=0.999,\n",
    "    )\n",
    "arg_scope = {\"order\": \"NCHW\"}\n",
    "train_model = model_helper.ModelHelper(name=\"mnist_train\", arg_scope=arg_scope)\n",
    "data, label = AddInput(\n",
    "    train_model, batch_size=64,\n",
    "    db=os.path.join(data_folder, 'mnist-train-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(train_model, data)\n",
    "AddTrainingOperators(train_model, softmax, label)\n",
    "workspace.ResetWorkspace()\n",
    "workspace.RunNetOnce(train_model.param_init_net)\n",
    "workspace.CreateNet(train_model.net, overwrite=True)\n",
    "total_iters_adam = 1000\n",
    "accuracy_adam = np.zeros(total_iters_adam)\n",
    "loss_adam = np.zeros(total_iters_adam)\n",
    "printmd('**Training status: Running**  Wait till the process is completed')\n",
    "for i in range(total_iters_adam):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed iterations:\", i, \", Total iterations to be completed:\", total_iters_adam\n",
    "    workspace.RunNet(train_model.net)\n",
    "    accuracy_adam[i] = workspace.blobs['accuracy']\n",
    "    loss_adam[i] = workspace.blobs['loss']\n",
    "#print \"Training completed\"\n",
    "printmd('**Training status: Completed**')\n",
    "\n",
    "#print \"Testing status: Running\"\n",
    "printmd('**Testing status: Running**  Wait till the process is completed')\n",
    "test_model = model_helper.ModelHelper(\n",
    "    name=\"mnist_test\", arg_scope=arg_scope, init_params=False)\n",
    "data, label = AddInput(\n",
    "    test_model, batch_size=100,\n",
    "    db=os.path.join(data_folder, 'mnist-test-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(test_model, data)\n",
    "AddAccuracy(test_model, softmax, label)\n",
    "workspace.RunNetOnce(test_model.param_init_net)\n",
    "workspace.CreateNet(test_model.net, overwrite=True)\n",
    "test_iters_adam = 1000\n",
    "test_accuracy_adam = np.zeros(test_iters_adam)\n",
    "for i in range(test_iters_adam):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed test set:\", i, \", Total sets to be tested:\", test_iters_adam\n",
    "    workspace.RunNet(test_model.net.Proto().name)\n",
    "    test_accuracy_adam[i] = workspace.FetchBlob('accuracy')\n",
    "#print \"Testing completed\"\n",
    "printmd('**Testing status: Completed**')\n",
    "# After the execution is done, let's plot the values.\n",
    "pyplot.plot(loss_adam, 'b')\n",
    "pyplot.plot(accuracy_adam, 'r')\n",
    "pyplot.legend(('Loss', 'Accuracy'), loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Effects of optimizers**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'loss_sgd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-e0b0701fa8a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_sgd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_sgd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss_sgd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Accuracy_sgd'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'upper right'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'loss_sgd' is not defined"
     ]
    }
   ],
   "source": [
    "printmd('**Effects of optimizers**')\n",
    "\n",
    "pyplot.figure(figsize=(10, 5))\n",
    "pyplot.subplot(3,1,1)\n",
    "pyplot.plot(loss_sgd, 'b')\n",
    "pyplot.plot(accuracy_sgd, 'r')\n",
    "pyplot.legend(('Loss_sgd', 'Accuracy_sgd'), loc='upper right')\n",
    "\n",
    "\n",
    "pyplot.subplot(3,1,2)\n",
    "pyplot.plot(loss_sgd_pr, 'b')\n",
    "pyplot.plot(accuracy_sgd_pr, 'r')\n",
    "pyplot.legend(('Loss_sgd_pr', 'Accuracy_sgd_pr'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(3,1,3)\n",
    "pyplot.plot(loss_adam, 'b')\n",
    "pyplot.plot(accuracy_adam, 'r')\n",
    "pyplot.legend(('Loss_adam', 'Accuracy_adam'), loc='upper right')\n",
    "\n",
    "\n",
    "solvers = [\n",
    "    ['Solver Type', 'Iterations to converge', 'Init LR', 'Init Loss', 'Final Loss', 'Init Training-Acc(%)', \\\n",
    "     'Final Training-Acc(%)', 'Final Testing-Acc(%)'],\n",
    "    ['SGD', total_iters_sgd, base_lr, loss_sgd[0], loss_sgd[total_iters_sgd-1], accuracy_sgd[0]*100, \\\n",
    "     accuracy_sgd[total_iters_sgd-1]*100, np.mean(test_accuracy_sgd)*100],\n",
    "    ['ADAM', total_iters_adam, base_lr, loss_adam[0], loss_adam[total_iters_adam-1], accuracy_adam[0]*100, \\\n",
    "     accuracy_adam[total_iters_adam-1]*100, np.mean(test_accuracy_adam)*100]\n",
    "\n",
    "];\n",
    "make_table(solvers)\n",
    "apply_theme('basic')\n",
    "set_cell_style(3, 7, color='red')\n",
    "set_cell_style(3, 1, color='red')\n",
    "set_cell_style(3, 0, color='yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver: Adagrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Training status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed iterations: 0 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 100 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 200 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 300 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 400 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 500 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 600 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 700 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 800 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 900 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1000 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1100 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1200 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1300 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1400 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1500 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1600 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1700 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1800 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1900 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2000 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2100 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2200 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2300 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2400 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2500 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2600 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2700 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2800 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2900 , Total iterations to be completed: 3000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Training status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed test set: 0 , Total sets to be tested: 1000\n",
      "    Completed test set: 100 , Total sets to be tested: 1000\n",
      "    Completed test set: 200 , Total sets to be tested: 1000\n",
      "    Completed test set: 300 , Total sets to be tested: 1000\n",
      "    Completed test set: 400 , Total sets to be tested: 1000\n",
      "    Completed test set: 500 , Total sets to be tested: 1000\n",
      "    Completed test set: 600 , Total sets to be tested: 1000\n",
      "    Completed test set: 700 , Total sets to be tested: 1000\n",
      "    Completed test set: 800 , Total sets to be tested: 1000\n",
      "    Completed test set: 900 , Total sets to be tested: 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ed5e64e55d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e5f9db50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.001\n",
    "def AddTrainingOperators(model, softmax, label):\n",
    "    xent = model.LabelCrossEntropy([softmax, label], 'xent')\n",
    "    loss = model.AveragedLoss(xent, \"loss\")\n",
    "    AddAccuracy(model, softmax, label)\n",
    "    model.AddGradientOperators([loss])\n",
    "    optimizer.build_adagrad(\n",
    "        model,\n",
    "        base_learning_rate=base_lr, \n",
    "        decay=1, \n",
    "        epsilon=1e-4,\n",
    "        policy=\"step\",\n",
    "        stepsize=1,\n",
    "        gamma=0.999,\n",
    "    )\n",
    "arg_scope = {\"order\": \"NCHW\"}\n",
    "train_model = model_helper.ModelHelper(name=\"mnist_train\", arg_scope=arg_scope)\n",
    "data, label = AddInput(\n",
    "    train_model, batch_size=64,\n",
    "    db=os.path.join(data_folder, 'mnist-train-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(train_model, data)\n",
    "AddTrainingOperators(train_model, softmax, label)\n",
    "workspace.ResetWorkspace()\n",
    "workspace.RunNetOnce(train_model.param_init_net)\n",
    "workspace.CreateNet(train_model.net, overwrite=True)\n",
    "total_iters_adagrad = 3000\n",
    "accuracy_adagrad = np.zeros(total_iters_adagrad)\n",
    "loss_adagrad = np.zeros(total_iters_adagrad)\n",
    "printmd('**Training status: Running**  Wait till the process is completed')\n",
    "for i in range(total_iters_adagrad):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed iterations:\", i, \", Total iterations to be completed:\", total_iters_adagrad\n",
    "    workspace.RunNet(train_model.net)\n",
    "    accuracy_adagrad[i] = workspace.blobs['accuracy']\n",
    "    loss_adagrad[i] = workspace.blobs['loss']\n",
    "#print \"Training completed\"\n",
    "printmd('**Training status: Completed**')\n",
    "\n",
    "#print \"Testing status: Running\"\n",
    "printmd('**Testing status: Running**  Wait till the process is completed')\n",
    "test_model = model_helper.ModelHelper(\n",
    "    name=\"mnist_test\", arg_scope=arg_scope, init_params=False)\n",
    "data, label = AddInput(\n",
    "    test_model, batch_size=100,\n",
    "    db=os.path.join(data_folder, 'mnist-test-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(test_model, data)\n",
    "AddAccuracy(test_model, softmax, label)\n",
    "workspace.RunNetOnce(test_model.param_init_net)\n",
    "workspace.CreateNet(test_model.net, overwrite=True)\n",
    "test_iters_adagrad = 1000\n",
    "test_accuracy_adagrad = np.zeros(test_iters_adagrad)\n",
    "for i in range(test_iters_adagrad):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed test set:\", i, \", Total sets to be tested:\", test_iters_adagrad\n",
    "    workspace.RunNet(test_model.net.Proto().name)\n",
    "    test_accuracy_adagrad[i] = workspace.FetchBlob('accuracy')\n",
    "#print \"Testing completed\"\n",
    "printmd('**Testing status: Completed**')\n",
    "# After the execution is done, let's plot the values.\n",
    "pyplot.plot(loss_adagrad, 'b')\n",
    "pyplot.plot(accuracy_adagrad, 'r')\n",
    "pyplot.legend(('Loss', 'Accuracy'), loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Effects of optimizers**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\"  style=\"border:black; border-collapse:collapse;\"><tr><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Solver&nbsp;Type</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Iterations&nbsp;to&nbsp;converge</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;LR</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Testing-Acc(%)</b></td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">SGD</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3395</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1706</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">10.9375</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.1900</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">Multi-Precision-SGD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3698</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1656</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">9.3750</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.5000</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAM</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3740</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0167</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">14.0625</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.6500</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAGRAD</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">3000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3726</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1954</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">12.5000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.3125</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.5900</td></tr></table>"
      ],
      "text/plain": [
       "<ipy_table.ipy_table.IpyTable at 0x7ed5e6232810>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e4141210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('**Effects of optimizers**')\n",
    "\n",
    "pyplot.figure(figsize=(10, 10))\n",
    "pyplot.subplot(4,1,1)\n",
    "pyplot.plot(loss_sgd, 'b')\n",
    "pyplot.plot(accuracy_sgd, 'r')\n",
    "pyplot.legend(('Loss_sgd', 'Accuracy_sgd'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(4,1,2)\n",
    "pyplot.plot(loss_sgd_pr, 'b')\n",
    "pyplot.plot(accuracy_sgd_pr, 'r')\n",
    "pyplot.legend(('Loss_sgd_pr', 'Accuracy_sgd_pr'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(4,1,3)\n",
    "pyplot.plot(loss_adam, 'b')\n",
    "pyplot.plot(accuracy_adam, 'r')\n",
    "pyplot.legend(('Loss_adam', 'Accuracy_adam'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(4,1,4)\n",
    "pyplot.plot(loss_adagrad, 'b')\n",
    "pyplot.plot(accuracy_adagrad, 'r')\n",
    "pyplot.legend(('Loss_adagrad', 'Accuracy_adagrad'), loc='upper right')\n",
    "\n",
    "\n",
    "solvers = [\n",
    "    ['Solver Type', 'Iterations to converge', 'Init LR', 'Init Loss', 'Final Loss', 'Init Training-Acc(%)', \\\n",
    "     'Final Training-Acc(%)', 'Final Testing-Acc(%)'],\n",
    "    ['SGD', total_iters_sgd, base_lr, loss_sgd[0], loss_sgd[total_iters_sgd-1], accuracy_sgd[0]*100, \\\n",
    "     accuracy_sgd[total_iters_sgd-1]*100, np.mean(test_accuracy_sgd)*100],\n",
    "    ['Multi-Precision-SGD', total_iters_sgd, base_lr, loss_sgd_pr[0], loss_sgd_pr[total_iters_sgd_pr-1], \\\n",
    "     accuracy_sgd_pr[0]*100, accuracy_sgd_pr[total_iters_sgd_pr-1]*100, np.mean(test_accuracy_sgd_pr)*100],\n",
    "    ['ADAM', total_iters_adam, base_lr, loss_adam[0], loss_adam[total_iters_adam-1], accuracy_adam[0]*100, \\\n",
    "     accuracy_adam[total_iters_adam-1]*100, np.mean(test_accuracy_adam)*100],\n",
    "    ['ADAGRAD', total_iters_adagrad, base_lr, loss_adagrad[0], loss_adagrad[total_iters_adagrad-1], \\\n",
    "     accuracy_adagrad[0]*100, accuracy_adagrad[total_iters_adagrad-1]*100, np.mean(test_accuracy_adagrad)*100]\n",
    "\n",
    "];\n",
    "make_table(solvers)\n",
    "apply_theme('basic')\n",
    "set_cell_style(3, 7, color='red')\n",
    "set_cell_style(3, 1, color='red')\n",
    "set_cell_style(4, 1, color='yellow')\n",
    "set_cell_style(4, 7, color='yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver: RMS-Prop\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Training status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed iterations: 0 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 100 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 200 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 300 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 400 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 500 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 600 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 700 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 800 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 900 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 1000 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 1100 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 1200 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 1300 , Total iterations to be completed: 1500\n",
      "    Completed iterations: 1400 , Total iterations to be completed: 1500\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Training status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed test set: 0 , Total sets to be tested: 1000\n",
      "    Completed test set: 100 , Total sets to be tested: 1000\n",
      "    Completed test set: 200 , Total sets to be tested: 1000\n",
      "    Completed test set: 300 , Total sets to be tested: 1000\n",
      "    Completed test set: 400 , Total sets to be tested: 1000\n",
      "    Completed test set: 500 , Total sets to be tested: 1000\n",
      "    Completed test set: 600 , Total sets to be tested: 1000\n",
      "    Completed test set: 700 , Total sets to be tested: 1000\n",
      "    Completed test set: 800 , Total sets to be tested: 1000\n",
      "    Completed test set: 900 , Total sets to be tested: 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ed5e6100b10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e62322d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.001\n",
    "def AddTrainingOperators(model, softmax, label):\n",
    "    xent = model.LabelCrossEntropy([softmax, label], 'xent')\n",
    "    loss = model.AveragedLoss(xent, \"loss\")\n",
    "    AddAccuracy(model, softmax, label)\n",
    "    model.AddGradientOperators([loss])\n",
    "    optimizer.build_rms_prop(\n",
    "        model,\n",
    "        base_learning_rate=base_lr, \n",
    "        decay=0.9, \n",
    "        momentum=0.9,\n",
    "        epsilon=1e-5,\n",
    "        policy=\"step\",\n",
    "        stepsize=1,\n",
    "        gamma=0.999,\n",
    "    )\n",
    "arg_scope = {\"order\": \"NCHW\"}\n",
    "train_model = model_helper.ModelHelper(name=\"mnist_train\", arg_scope=arg_scope)\n",
    "data, label = AddInput(\n",
    "    train_model, batch_size=64,\n",
    "    db=os.path.join(data_folder, 'mnist-train-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(train_model, data)\n",
    "AddTrainingOperators(train_model, softmax, label)\n",
    "workspace.ResetWorkspace()\n",
    "workspace.RunNetOnce(train_model.param_init_net)\n",
    "workspace.CreateNet(train_model.net, overwrite=True)\n",
    "total_iters_rmsprop = 1500\n",
    "accuracy_rmsprop = np.zeros(total_iters_rmsprop)\n",
    "loss_rmsprop = np.zeros(total_iters_rmsprop)\n",
    "printmd('**Training status: Running**  Wait till the process is completed')\n",
    "for i in range(total_iters_rmsprop):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed iterations:\", i, \", Total iterations to be completed:\", total_iters_rmsprop\n",
    "    workspace.RunNet(train_model.net)\n",
    "    accuracy_rmsprop[i] = workspace.blobs['accuracy']\n",
    "    loss_rmsprop[i] = workspace.blobs['loss']\n",
    "#print \"Training completed\"\n",
    "printmd('**Training status: Completed**')\n",
    "\n",
    "#print \"Testing status: Running\"\n",
    "printmd('**Testing status: Running**  Wait till the process is completed')\n",
    "test_model = model_helper.ModelHelper(\n",
    "    name=\"mnist_test\", arg_scope=arg_scope, init_params=False)\n",
    "data, label = AddInput(\n",
    "    test_model, batch_size=100,\n",
    "    db=os.path.join(data_folder, 'mnist-test-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(test_model, data)\n",
    "AddAccuracy(test_model, softmax, label)\n",
    "workspace.RunNetOnce(test_model.param_init_net)\n",
    "workspace.CreateNet(test_model.net, overwrite=True)\n",
    "test_iters_rmsprop = 1000\n",
    "test_accuracy_rmsprop = np.zeros(test_iters_rmsprop)\n",
    "for i in range(test_iters_rmsprop):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed test set:\", i, \", Total sets to be tested:\", test_iters_rmsprop\n",
    "    workspace.RunNet(test_model.net.Proto().name)\n",
    "    test_accuracy_rmsprop[i] = workspace.FetchBlob('accuracy')\n",
    "#print \"Testing completed\"\n",
    "printmd('**Testing status: Completed**')\n",
    "# After the execution is done, let's plot the values.\n",
    "pyplot.plot(loss_rmsprop, 'b')\n",
    "pyplot.plot(accuracy_rmsprop, 'r')\n",
    "pyplot.legend(('Loss', 'Accuracy'), loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Effects of optimizers**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\"  style=\"border:black; border-collapse:collapse;\"><tr><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Solver&nbsp;Type</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Iterations&nbsp;to&nbsp;converge</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;LR</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Testing-Acc(%)</b></td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">SGD</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3395</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1706</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">10.9375</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.1900</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">Multi-Precision-SGD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3698</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1656</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">9.3750</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.5000</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAM</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3740</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0167</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">14.0625</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.6500</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAGRAD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">3000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3726</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1954</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">12.5000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.3125</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.5900</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">RMSPROP</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.2954</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0320</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">15.6250</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.6700</td></tr></table>"
      ],
      "text/plain": [
       "<ipy_table.ipy_table.IpyTable at 0x7ed5e6309290>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e6093ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('**Effects of optimizers**')\n",
    "\n",
    "pyplot.figure(figsize=(10, 10))\n",
    "pyplot.subplot(5,1,1)\n",
    "pyplot.plot(loss_sgd, 'b')\n",
    "pyplot.plot(accuracy_sgd, 'r')\n",
    "pyplot.legend(('Loss_sgd', 'Accuracy_sgd'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(5,1,2)\n",
    "pyplot.plot(loss_sgd_pr, 'b')\n",
    "pyplot.plot(accuracy_sgd_pr, 'r')\n",
    "pyplot.legend(('Loss_sgd_pr', 'Accuracy_sgd_pr'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(5,1,3)\n",
    "pyplot.plot(loss_adam, 'b')\n",
    "pyplot.plot(accuracy_adam, 'r')\n",
    "pyplot.legend(('Loss_adam', 'Accuracy_adam'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(5,1,4)\n",
    "pyplot.plot(loss_adagrad, 'b')\n",
    "pyplot.plot(accuracy_adagrad, 'r')\n",
    "pyplot.legend(('Loss_adagrad', 'Accuracy_adagrad'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(5,1,5)\n",
    "pyplot.plot(loss_rmsprop, 'b')\n",
    "pyplot.plot(accuracy_rmsprop, 'r')\n",
    "pyplot.legend(('Loss_rmsprop', 'Accuracy_rmsprop'), loc='upper right')\n",
    "\n",
    "\n",
    "solvers = [\n",
    "    ['Solver Type', 'Iterations to converge', 'Init LR', 'Init Loss', 'Final Loss', 'Init Training-Acc(%)', \\\n",
    "     'Final Training-Acc(%)', 'Final Testing-Acc(%)'],\n",
    "    ['SGD', total_iters_sgd, base_lr, loss_sgd[0], loss_sgd[total_iters_sgd-1], accuracy_sgd[0]*100, \\\n",
    "     accuracy_sgd[total_iters_sgd-1]*100, np.mean(test_accuracy_sgd)*100],\n",
    "    ['Multi-Precision-SGD', total_iters_sgd, base_lr, loss_sgd_pr[0], loss_sgd_pr[total_iters_sgd_pr-1], \\\n",
    "     accuracy_sgd_pr[0]*100, accuracy_sgd_pr[total_iters_sgd_pr-1]*100, np.mean(test_accuracy_sgd_pr)*100],\n",
    "    ['ADAM', total_iters_adam, base_lr, loss_adam[0], loss_adam[total_iters_adam-1], accuracy_adam[0]*100, \\\n",
    "     accuracy_adam[total_iters_adam-1]*100, np.mean(test_accuracy_adam)*100],\n",
    "    ['ADAGRAD', total_iters_adagrad, base_lr, loss_adagrad[0], loss_adagrad[total_iters_adagrad-1], \\\n",
    "     accuracy_adagrad[0]*100, accuracy_adagrad[total_iters_adagrad-1]*100, np.mean(test_accuracy_adagrad)*100],\n",
    "    ['RMSPROP', total_iters_rmsprop, base_lr, loss_rmsprop[0], loss_rmsprop[total_iters_rmsprop-1], \\\n",
    "     accuracy_rmsprop[0]*100, accuracy_rmsprop[total_iters_rmsprop-1]*100, np.mean(test_accuracy_rmsprop)*100]\n",
    "\n",
    "];\n",
    "make_table(solvers)\n",
    "apply_theme('basic')\n",
    "set_cell_style(3, 7, color='yellow')\n",
    "set_cell_style(3, 1, color='yellow')\n",
    "set_cell_style(5, 1, color='red')\n",
    "set_cell_style(5, 7, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Training status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed iterations: 0 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 100 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 200 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 300 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 400 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 500 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 600 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 700 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 800 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 900 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1000 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1100 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1200 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1300 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1400 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1500 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1600 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1700 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1800 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 1900 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2000 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2100 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2200 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2300 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2400 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2500 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2600 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2700 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2800 , Total iterations to be completed: 3000\n",
      "    Completed iterations: 2900 , Total iterations to be completed: 3000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Training status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed test set: 0 , Total sets to be tested: 1000\n",
      "    Completed test set: 100 , Total sets to be tested: 1000\n",
      "    Completed test set: 200 , Total sets to be tested: 1000\n",
      "    Completed test set: 300 , Total sets to be tested: 1000\n",
      "    Completed test set: 400 , Total sets to be tested: 1000\n",
      "    Completed test set: 500 , Total sets to be tested: 1000\n",
      "    Completed test set: 600 , Total sets to be tested: 1000\n",
      "    Completed test set: 700 , Total sets to be tested: 1000\n",
      "    Completed test set: 800 , Total sets to be tested: 1000\n",
      "    Completed test set: 900 , Total sets to be tested: 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ed5e62f2550>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e5659cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.001\n",
    "def AddTrainingOperators(model, softmax, label):\n",
    "    xent = model.LabelCrossEntropy([softmax, label], 'xent')\n",
    "    loss = model.AveragedLoss(xent, \"loss\")\n",
    "    AddAccuracy(model, softmax, label)\n",
    "    model.AddGradientOperators([loss])\n",
    "    optimizer.build_ftrl(\n",
    "        model,\n",
    "        engine=\"CPU\",\n",
    "        alpha=base_lr,\n",
    "        beta=1e-4,\n",
    "        lambda1=0., \n",
    "        lambda2=0.,\n",
    "    )\n",
    "arg_scope = {\"order\": \"NCHW\"}\n",
    "train_model = model_helper.ModelHelper(name=\"mnist_train\", arg_scope=arg_scope)\n",
    "data, label = AddInput(\n",
    "    train_model, batch_size=64,\n",
    "    db=os.path.join(data_folder, 'mnist-train-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(train_model, data)\n",
    "AddTrainingOperators(train_model, softmax, label)\n",
    "workspace.ResetWorkspace()\n",
    "workspace.RunNetOnce(train_model.param_init_net)\n",
    "workspace.CreateNet(train_model.net, overwrite=True)\n",
    "total_iters_ftrl = 3000\n",
    "accuracy_ftrl = np.zeros(total_iters_ftrl)\n",
    "loss_ftrl = np.zeros(total_iters_ftrl)\n",
    "printmd('**Training status: Running**  Wait till the process is completed')\n",
    "for i in range(total_iters_ftrl):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed iterations:\", i, \", Total iterations to be completed:\", total_iters_ftrl\n",
    "    workspace.RunNet(train_model.net)\n",
    "    accuracy_ftrl[i] = workspace.blobs['accuracy']\n",
    "    loss_ftrl[i] = workspace.blobs['loss']\n",
    "#print \"Training completed\"\n",
    "printmd('**Training status: Completed**')\n",
    "\n",
    "#print \"Testing status: Running\"\n",
    "printmd('**Testing status: Running**  Wait till the process is completed')\n",
    "test_model = model_helper.ModelHelper(\n",
    "    name=\"mnist_test\", arg_scope=arg_scope, init_params=False)\n",
    "data, label = AddInput(\n",
    "    test_model, batch_size=100,\n",
    "    db=os.path.join(data_folder, 'mnist-test-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(test_model, data)\n",
    "AddAccuracy(test_model, softmax, label)\n",
    "workspace.RunNetOnce(test_model.param_init_net)\n",
    "workspace.CreateNet(test_model.net, overwrite=True)\n",
    "test_iters_ftrl = 1000\n",
    "test_accuracy_ftrl = np.zeros(test_iters_ftrl)\n",
    "for i in range(test_iters_ftrl):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed test set:\", i, \", Total sets to be tested:\", test_iters_ftrl\n",
    "    workspace.RunNet(test_model.net.Proto().name)\n",
    "    test_accuracy_ftrl[i] = workspace.FetchBlob('accuracy')\n",
    "#print \"Testing completed\"\n",
    "printmd('**Testing status: Completed**')\n",
    "# After the execution is done, let's plot the values.\n",
    "pyplot.plot(loss_ftrl, 'b')\n",
    "pyplot.plot(accuracy_ftrl, 'r')\n",
    "pyplot.legend(('Loss', 'Accuracy'), loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Effects of optimizers**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\"  style=\"border:black; border-collapse:collapse;\"><tr><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Solver&nbsp;Type</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Iterations&nbsp;to&nbsp;converge</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;LR</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Testing-Acc(%)</b></td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">SGD</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3395</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1706</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">10.9375</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.1900</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">Multi-Precision-SGD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3698</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1656</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">9.3750</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.5000</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAM</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3740</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0167</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">14.0625</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.6500</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAGRAD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">3000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3726</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1954</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">12.5000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.3125</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.5900</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">RMSPROP</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.2954</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0320</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">15.6250</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.6700</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">FTRL</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">3000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.5421</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0839</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">7.8125</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">96.7700</td></tr></table>"
      ],
      "text/plain": [
       "<ipy_table.ipy_table.IpyTable at 0x7ed5e543be50>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e62d1d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('**Effects of optimizers**')\n",
    "\n",
    "pyplot.figure(figsize=(10, 10))\n",
    "pyplot.subplot(6,1,1)\n",
    "pyplot.plot(loss_sgd, 'b')\n",
    "pyplot.plot(accuracy_sgd, 'r')\n",
    "pyplot.legend(('Loss_sgd', 'Accuracy_sgd'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(6,1,2)\n",
    "pyplot.plot(loss_sgd_pr, 'b')\n",
    "pyplot.plot(accuracy_sgd_pr, 'r')\n",
    "pyplot.legend(('Loss_sgd_pr', 'Accuracy_sgd_pr'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(6,1,3)\n",
    "pyplot.plot(loss_adam, 'b')\n",
    "pyplot.plot(accuracy_adam, 'r')\n",
    "pyplot.legend(('Loss_adam', 'Accuracy_adam'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(6,1,4)\n",
    "pyplot.plot(loss_adagrad, 'b')\n",
    "pyplot.plot(accuracy_adagrad, 'r')\n",
    "pyplot.legend(('Loss_adagrad', 'Accuracy_adagrad'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(6,1,5)\n",
    "pyplot.plot(loss_rmsprop, 'b')\n",
    "pyplot.plot(accuracy_rmsprop, 'r')\n",
    "pyplot.legend(('Loss_rmsprop', 'Accuracy_rmsprop'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(6,1,6)\n",
    "pyplot.plot(loss_ftrl, 'b')\n",
    "pyplot.plot(accuracy_ftrl, 'r')\n",
    "pyplot.legend(('Loss_ftrl', 'Accuracy_ftrl'), loc='upper right')\n",
    "\n",
    "\n",
    "solvers = [\n",
    "    ['Solver Type', 'Iterations to converge', 'Init LR', 'Init Loss', 'Final Loss', 'Init Training-Acc(%)', \\\n",
    "     'Final Training-Acc(%)', 'Final Testing-Acc(%)'],\n",
    "    ['SGD', total_iters_sgd, base_lr, loss_sgd[0], loss_sgd[total_iters_sgd-1], accuracy_sgd[0]*100, \\\n",
    "     accuracy_sgd[total_iters_sgd-1]*100, np.mean(test_accuracy_sgd)*100],\n",
    "    ['Multi-Precision-SGD', total_iters_sgd, base_lr, loss_sgd_pr[0], loss_sgd_pr[total_iters_sgd_pr-1], \\\n",
    "     accuracy_sgd_pr[0]*100, accuracy_sgd_pr[total_iters_sgd_pr-1]*100, np.mean(test_accuracy_sgd_pr)*100],\n",
    "    ['ADAM', total_iters_adam, base_lr, loss_adam[0], loss_adam[total_iters_adam-1], accuracy_adam[0]*100, \\\n",
    "     accuracy_adam[total_iters_adam-1]*100, np.mean(test_accuracy_adam)*100],\n",
    "    ['ADAGRAD', total_iters_adagrad, base_lr, loss_adagrad[0], loss_adagrad[total_iters_adagrad-1], \\\n",
    "     accuracy_adagrad[0]*100, accuracy_adagrad[total_iters_adagrad-1]*100, np.mean(test_accuracy_adagrad)*100],\n",
    "    ['RMSPROP', total_iters_rmsprop, base_lr, loss_rmsprop[0], loss_rmsprop[total_iters_rmsprop-1], \\\n",
    "     accuracy_rmsprop[0]*100, accuracy_rmsprop[total_iters_rmsprop-1]*100, np.mean(test_accuracy_rmsprop)*100],\n",
    "    ['FTRL', total_iters_ftrl, base_lr, loss_ftrl[0], loss_ftrl[total_iters_ftrl-1], \\\n",
    "     accuracy_ftrl[0]*100, accuracy_ftrl[total_iters_ftrl-1]*100, np.mean(test_accuracy_ftrl)*100]\n",
    "\n",
    "];\n",
    "make_table(solvers)\n",
    "apply_theme('basic')\n",
    "set_cell_style(3, 7, color='yellow')\n",
    "set_cell_style(3, 1, color='yellow')\n",
    "set_cell_style(5, 1, color='red')\n",
    "set_cell_style(5, 7, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Training status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed iterations: 0 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 100 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 200 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 300 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 400 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 500 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 600 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 700 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 800 , Total iterations to be completed: 1000\n",
      "    Completed iterations: 900 , Total iterations to be completed: 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Training status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Running**  Wait till the process is completed"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Completed test set: 0 , Total sets to be tested: 1000\n",
      "    Completed test set: 100 , Total sets to be tested: 1000\n",
      "    Completed test set: 200 , Total sets to be tested: 1000\n",
      "    Completed test set: 300 , Total sets to be tested: 1000\n",
      "    Completed test set: 400 , Total sets to be tested: 1000\n",
      "    Completed test set: 500 , Total sets to be tested: 1000\n",
      "    Completed test set: 600 , Total sets to be tested: 1000\n",
      "    Completed test set: 700 , Total sets to be tested: 1000\n",
      "    Completed test set: 800 , Total sets to be tested: 1000\n",
      "    Completed test set: 900 , Total sets to be tested: 1000\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**Testing status: Completed**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ed5e54f83d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e71bd6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_lr = 0.001\n",
    "base_lr_yellowfin = 0.01\n",
    "def AddTrainingOperators(model, softmax, label):\n",
    "    xent = model.LabelCrossEntropy([softmax, label], 'xent')\n",
    "    loss = model.AveragedLoss(xent, \"loss\")\n",
    "    AddAccuracy(model, softmax, label)\n",
    "    model.AddGradientOperators([loss])\n",
    "    optimizer.build_yellowfin(\n",
    "        model,\n",
    "        base_learning_rate=base_lr_yellowfin, \n",
    "        mu=0.0,\n",
    "        beta=0.999,\n",
    "        curv_win_width=20,\n",
    "        zero_debias=True,\n",
    "        epsilon=0.1**6,\n",
    "        policy=\"step\",\n",
    "        stepsize=1,\n",
    "        gamma=0.999,\n",
    "    )\n",
    "arg_scope = {\"order\": \"NCHW\"}\n",
    "train_model = model_helper.ModelHelper(name=\"mnist_train\", arg_scope=arg_scope)\n",
    "data, label = AddInput(\n",
    "    train_model, batch_size=64,\n",
    "    db=os.path.join(data_folder, 'mnist-train-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(train_model, data)\n",
    "AddTrainingOperators(train_model, softmax, label)\n",
    "workspace.ResetWorkspace()\n",
    "workspace.RunNetOnce(train_model.param_init_net)\n",
    "workspace.CreateNet(train_model.net, overwrite=True)\n",
    "total_iters_yellowfin = 1000\n",
    "accuracy_yellowfin = np.zeros(total_iters_yellowfin)\n",
    "loss_yellowfin = np.zeros(total_iters_yellowfin)\n",
    "printmd('**Training status: Running**  Wait till the process is completed')\n",
    "for i in range(total_iters_yellowfin):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed iterations:\", i, \", Total iterations to be completed:\", total_iters_yellowfin\n",
    "    workspace.RunNet(train_model.net)\n",
    "    accuracy_yellowfin[i] = workspace.blobs['accuracy']\n",
    "    loss_yellowfin[i] = workspace.blobs['loss']\n",
    "#print \"Training completed\"\n",
    "printmd('**Training status: Completed**')\n",
    "\n",
    "#print \"Testing status: Running\"\n",
    "printmd('**Testing status: Running**  Wait till the process is completed')\n",
    "test_model = model_helper.ModelHelper(\n",
    "    name=\"mnist_test\", arg_scope=arg_scope, init_params=False)\n",
    "data, label = AddInput(\n",
    "    test_model, batch_size=100,\n",
    "    db=os.path.join(data_folder, 'mnist-test-nchw-lmdb'),\n",
    "    db_type='lmdb')\n",
    "softmax = AddModel(test_model, data)\n",
    "AddAccuracy(test_model, softmax, label)\n",
    "workspace.RunNetOnce(test_model.param_init_net)\n",
    "workspace.CreateNet(test_model.net, overwrite=True)\n",
    "test_iters_yellowfin = 1000\n",
    "test_accuracy_yellowfin = np.zeros(test_iters_yellowfin)\n",
    "for i in range(test_iters_yellowfin):\n",
    "    if(i%100 == 0):\n",
    "        print \"    Completed test set:\", i, \", Total sets to be tested:\", test_iters_yellowfin\n",
    "    workspace.RunNet(test_model.net.Proto().name)\n",
    "    test_accuracy_yellowfin[i] = workspace.FetchBlob('accuracy')\n",
    "#print \"Testing completed\"\n",
    "printmd('**Testing status: Completed**')\n",
    "# After the execution is done, let's plot the values.\n",
    "pyplot.plot(loss_yellowfin, 'b')\n",
    "pyplot.plot(accuracy_yellowfin, 'r')\n",
    "pyplot.legend(('Loss', 'Accuracy'), loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Effects of optimizers**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\"  style=\"border:black; border-collapse:collapse;\"><tr><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Solver&nbsp;Type</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Iterations&nbsp;to&nbsp;converge</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;LR</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Loss</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Init&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Training-Acc(%)</b></td><td  style=\"background-color:LightGray;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>Final&nbsp;Testing-Acc(%)</b></td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">SGD</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3395</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1706</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">10.9375</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.1900</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">Multi-Precision-SGD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3698</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1656</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">9.3750</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">93.7500</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">94.5000</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAM</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1000</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3740</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0167</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">14.0625</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:yellow;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.6500</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">ADAGRAD</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">3000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3726</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1954</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">12.5000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.3125</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">95.5900</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">RMSPROP</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1500</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.2954</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0320</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">15.6250</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:red;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.6700</td></tr><tr><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">FTRL</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">3000</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0010</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.5421</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0839</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">7.8125</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">98.4375</td><td  style=\"background-color:AliceBlue;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">96.7700</td></tr><tr><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">YELLOWFIN</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1000</td><td  style=\"background-color:green;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0100</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.3442</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0161</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">4.6875</td><td  style=\"background-color:Ivory;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">100.0000</td><td  style=\"background-color:green;border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">96.8900</td></tr></table>"
      ],
      "text/plain": [
       "<ipy_table.ipy_table.IpyTable at 0x7dd5dc65f350>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ed5e643ad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('**Effects of optimizers**')\n",
    "\n",
    "pyplot.figure(figsize=(10, 15))\n",
    "pyplot.subplot(7,1,1)\n",
    "pyplot.plot(loss_sgd, 'b')\n",
    "pyplot.plot(accuracy_sgd, 'r')\n",
    "pyplot.legend(('Loss_sgd', 'Accuracy_sgd'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(7,1,2)\n",
    "pyplot.plot(loss_sgd_pr, 'b')\n",
    "pyplot.plot(accuracy_sgd_pr, 'r')\n",
    "pyplot.legend(('Loss_sgd_pr', 'Accuracy_sgd_pr'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(7,1,3)\n",
    "pyplot.plot(loss_adam, 'b')\n",
    "pyplot.plot(accuracy_adam, 'r')\n",
    "pyplot.legend(('Loss_adam', 'Accuracy_adam'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(7,1,4)\n",
    "pyplot.plot(loss_adagrad, 'b')\n",
    "pyplot.plot(accuracy_adagrad, 'r')\n",
    "pyplot.legend(('Loss_adagrad', 'Accuracy_adagrad'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(7,1,5)\n",
    "pyplot.plot(loss_rmsprop, 'b')\n",
    "pyplot.plot(accuracy_rmsprop, 'r')\n",
    "pyplot.legend(('Loss_rmsprop', 'Accuracy_rmsprop'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(7,1,6)\n",
    "pyplot.plot(loss_ftrl, 'b')\n",
    "pyplot.plot(accuracy_ftrl, 'r')\n",
    "pyplot.legend(('Loss_ftrl', 'Accuracy_ftrl'), loc='upper right')\n",
    "\n",
    "pyplot.subplot(7,1,7)\n",
    "pyplot.plot(loss_yellowfin, 'b')\n",
    "pyplot.plot(accuracy_yellowfin, 'r')\n",
    "pyplot.legend(('Loss_yellowfin', 'Accuracy_yellowfin'), loc='upper right')\n",
    "\n",
    "\n",
    "solvers = [\n",
    "    ['Solver Type', 'Iterations to converge', 'Init LR', 'Init Loss', 'Final Loss', 'Init Training-Acc(%)', \\\n",
    "     'Final Training-Acc(%)', 'Final Testing-Acc(%)'],\n",
    "    ['SGD', total_iters_sgd, base_lr, loss_sgd[0], loss_sgd[total_iters_sgd-1], accuracy_sgd[0]*100, \\\n",
    "     accuracy_sgd[total_iters_sgd-1]*100, np.mean(test_accuracy_sgd)*100],\n",
    "    ['Multi-Precision-SGD', total_iters_sgd, base_lr, loss_sgd_pr[0], loss_sgd_pr[total_iters_sgd_pr-1], \\\n",
    "     accuracy_sgd_pr[0]*100, accuracy_sgd_pr[total_iters_sgd_pr-1]*100, np.mean(test_accuracy_sgd_pr)*100],\n",
    "    ['ADAM', total_iters_adam, base_lr, loss_adam[0], loss_adam[total_iters_adam-1], accuracy_adam[0]*100, \\\n",
    "     accuracy_adam[total_iters_adam-1]*100, np.mean(test_accuracy_adam)*100],\n",
    "    ['ADAGRAD', total_iters_adagrad, base_lr, loss_adagrad[0], loss_adagrad[total_iters_adagrad-1], \\\n",
    "     accuracy_adagrad[0]*100, accuracy_adagrad[total_iters_adagrad-1]*100, np.mean(test_accuracy_adagrad)*100],\n",
    "    ['RMSPROP', total_iters_rmsprop, base_lr, loss_rmsprop[0], loss_rmsprop[total_iters_rmsprop-1], \\\n",
    "     accuracy_rmsprop[0]*100, accuracy_rmsprop[total_iters_rmsprop-1]*100, np.mean(test_accuracy_rmsprop)*100],\n",
    "    ['FTRL', total_iters_ftrl, base_lr, loss_ftrl[0], loss_ftrl[total_iters_ftrl-1], \\\n",
    "     accuracy_ftrl[0]*100, accuracy_ftrl[total_iters_ftrl-1]*100, np.mean(test_accuracy_ftrl)*100], \n",
    "    ['YELLOWFIN', total_iters_yellowfin, base_lr_yellowfin, loss_yellowfin[0], loss_yellowfin[total_iters_yellowfin-1], \\\n",
    "     accuracy_yellowfin[0]*100, accuracy_yellowfin[total_iters_yellowfin-1]*100, np.mean(test_accuracy_yellowfin)*100]\n",
    "\n",
    "];\n",
    "make_table(solvers)\n",
    "apply_theme('basic')\n",
    "set_cell_style(3, 7, color='yellow')\n",
    "set_cell_style(3, 1, color='yellow')\n",
    "set_cell_style(5, 1, color='red')\n",
    "set_cell_style(5, 7, color='red')\n",
    "set_cell_style(7, 7, color='green')\n",
    "set_cell_style(7, 2, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
